Search Results for author:
Found 9 papers, 2 papers with code
Evaluating Roadside Perception for Autonomous Vehicles: Insights from Field Testing
This paper introduces a comprehensive evaluation methodology specifically designed to assess the performance of roadside perception systems.
MSight: An Edge-Cloud Infrastructure-based Perception System for Connected Automated Vehicles
As vehicular communication and networking technologies continue to advance, infrastructure-based roadside perception emerges as a pivotal tool for connected automated vehicle (CAV) applications.
Robust Roadside Perception: an Automated Data Synthesis Pipeline Minimizing Human Annotation
Our approach was rigorously tested at two key intersections in Michigan, USA: the Mcity intersection and the State St./Ellsworth Rd roundabout.
ROCO: A Roundabout Traffic Conflict Dataset
With the traffic conflict data collected, we discover that failure to yield to circulating vehicles when entering the roundabout is the largest contributing reason for traffic conflicts.
Real-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Cameras
We propose a novel and pragmatic framework for traffic scene perception with roadside cameras.
Using AI for Mitigating the Impact of Network Delay in Cloud-based Intelligent Traffic Signal Control
The recent advancements in cloud services, Internet of Things (IoT) and Cellular networks have made cloud computing an attractive option for intelligent traffic signal control (ITSC).
Leader selection in Vehicular Ad-hoc Networks: a Proactive Approach
A robust leader selection algorithm is required in a number of V2V communications related applications.
Distributed, Parallel, and Cluster Computing
Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation
Partially Detected Intelligent Traffic Signal Control (PD-ITSC) systems that can optimize traffic signals based on limited detected information could be a cost-efficient solution for mitigating traffic congestion in the future.
Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control
We examine a family of transportation systems, which we will refer to as `Partially Detected Intelligent Transportation Systems'.
